A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters

Yijie Peng, Michael C. Fu, Jian Qiang Hu, Bernd Heidergott

Research output: Contribution to JournalArticle

Abstract

In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the singlerun efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments.

Original languageEnglish
Pages (from-to)487-499
Number of pages13
JournalOperations Research
Volume66
Issue number2
DOIs
Publication statusPublished - Mar 2018

    Fingerprint

Keywords

  • Discontinuous sample performance
  • Likelihood ratio
  • Perturbation analysis
  • Simulation
  • Stochastic derivative estimation
  • Weak derivative

Cite this